April 7, 2000 / Vol. 49 / No. 13 U.S. DEPARTMENT OF HEALTH & HUMAN SERVICES National Infant Immunization Week — April 16–22, 2000 National Infant Immunization Week (NIIW) is April 16–22, 2000; this year’s theme is “You Gave Them Life...Protect It.” This week emphasizes the importance of timely infant and childhood vaccination. Vaccination is one of the most effective ways to protect children, especially infants and young children, from potentially serious diseases. Because of increased vaccination efforts in the United States, eight vaccine-preventable diseases are at or near record low levels. In 1999, 86 measles cases, eight congenital rubella cases, one diphtheria case, and no wild poliovirus cases were reported (1,2 ). Approximately 11,000 babies are born each day in the United States; they need 16–20 doses of vaccine before age 2 years. Although vaccination coverage levels are high for preschool-aged children, approximately 1 million 2-year-old children are missing one or more of the recommended vaccine doses (3 ). During NIIW, states and communities will sponsor activities designed to highlight the need to achieve and maintain high childhood vaccination coverage rates. In addi- tion, CDC will launch a new television public service announcement (PSA) and two radio PSAs in Spanish. Additional information about NIIW and childhood vaccinations is available from CDC’s National Immunization Program World-Wide Web site, http://www.cdc.gov/nip or the National Immunization Information Hotline, telephone (800) 232-2522 (English) or (800) 232-0233 (Spanish). References 1. CDC. Summary—provisional cases of selected notifiable diseases, United States, cumulative, week ending January 1, 2000 (52nd week). MMWR 2000;48:1183. 2. Table III. Provisional cases of selected notifiable disease preventable by vaccination, United States, weeks ending January 1, 2000, and January 2, 1999 (52nd week). MMWR 2000;48:1188. 3. CDC. National vaccination coverage levels among children aged 19–35 months—United States, 1998. MMWR 1999;48:829–30. 273 National Infant Immunization Week — April 16–22, 2000 274 Progress in Development of Immunization Registries — United States, 1999 278 Palmar Pallor as an Indicator for Anthelminthic Treatment Among Ill Children Aged 2–4 Years — Western Kenya, 1998 281 Community Indicators of Health- Related Quality of Life — United States, 1993–1997
Transcript
April 7, 2000 / Vol. 49 / No. 13
U.S. DEPARTMENT OF HEALTH & HUMAN SERVICES
National Infant Immunization Week — April 16–22, 2000
National Infant Immunization Week (NIIW) is April 16–22, 2000; this year’s themeis “You Gave Them Life...Protect It.” This week emphasizes the importance of timelyinfant and childhood vaccination. Vaccination is one of the most effective ways toprotect children, especially infants and young children, from potentially seriousdiseases. Because of increased vaccination efforts in the United States, eightvaccine-preventable diseases are at or near record low levels. In 1999, 86 measlescases, eight congenital rubella cases, one diphtheria case, and no wild polioviruscases were reported (1,2 ).
Approximately 11,000 babies are born each day in the United States; they need16–20 doses of vaccine before age 2 years. Although vaccination coverage levelsare high for preschool-aged children, approximately 1 million 2-year-old children aremissing one or more of the recommended vaccine doses (3 ).
During NIIW, states and communities will sponsor activities designed to highlightthe need to achieve and maintain high childhood vaccination coverage rates. In addi-tion, CDC will launch a new television public service announcement (PSA) and tworadio PSAs in Spanish. Additional information about NIIW and childhood vaccinationsis available from CDC’s National Immunization Program World-Wide Web site,http://www.cdc.gov/nip or the National Immunization Information Hotline, telephone(800) 232-2522 (English) or (800) 232-0233 (Spanish).References1. CDC. Summary—provisional cases of selected notifiable diseases, United States,
cumulative, week ending January 1, 2000 (52nd week). MMWR 2000;48:1183.2. Table III. Provisional cases of selected notifiable disease preventable by vaccination,
United States, weeks ending January 1, 2000, and January 2, 1999 (52nd week). MMWR2000;48:1188.
3. CDC. National vaccination coverage levels among children aged 19–35 months—UnitedStates, 1998. MMWR 1999;48:829–30.
273 National Infant Immunization Week —April 16–22, 2000
274 Progress in Development ofImmunization Registries —United States, 1999
278 Palmar Pallor as an Indicator forAnthelminthic Treatment Among IllChildren Aged 2–4 Years —Western Kenya, 1998
281 Community Indicators of Health-Related Quality of Life —United States, 1993–1997
Progress in Development of Immunization Registries —United States, 1999
Community-based and state-based immunization registries are confidential,population-based, computerized information systems that contain data about children’svaccinations (1 ) and represent an important tool to increase and sustain high vaccinationcoverage. Immunization registries consolidate vaccination records for children frommultiple providers, provide a vaccination needs assessment for each child, generatereminder and recall vaccination notices, produce an official vaccination record, and pro-vide practice-specific and community-based vaccination coverage assessments. One ofthe Healthy People 2010 national objectives is to increase to 95% the proportion ofchildren aged <6 years who are enrolled in a fully operational population-based immuni-zation registry (2 ). To assess the status of immunization registry development, CDCanalyzed data from the 1999 Immunization Registry Annual Report (IRAR) of 64 jurisdic-tions (grantees) that receive federal immunization funds under section 317d of the PublicHealth Service Act. Findings from this analysis indicate that substantial progress hasbeen made in the United States in developing and implementing community-based andstate-based immunization registries.
The IRAR was a self-administered questionnaire, sent to immunization program man-agers, that measured the degree of enrollment of a registry’s target population (i.e.,percentage of children in the catchment area with vaccinations recorded in the registryand percentage of public and private providers submitting records to the registry) andthe implementation of 12 functional standards considered essential for immunizationregistry operation. The 12 standards (Table 1) were identified through a survey of immu-nization program managers and registry developers. Focus group research with themanagers and developers was conducted to ensure consensus about the importance ofthese standards. Key elements associated with each standard then were identified andused to establish more sensitive registry development and implementation progressmeasures. In addition, the IRAR collected information on immunization registry links withother information systems.
In 1999, the 64 jurisdictions (50 states, the District of Columbia, Chicago, Houston,New York City, Philadelphia, San Antonio, American Samoa, Guam, Marshall Islands,Micronesia, Northern Mariana Islands, Palau, Puerto Rico, and the U.S. Virgin Islands)were mailed the questionnaire; 62 (97%) responded. Of the 62, three (5%) grantees (allcommonwealths or territories) reported no registry activity, 16 (26%) grantees reportedplanning or pilot-testing of registries, and 43 (69%) grantees reported implementingregistries (Figure 1).
Data from 37 of the 43 grantees implementing registries indicated that approxi-mately 32% (mean=50%; median=54%) of estimated target children aged 0–5 years inthe grantees’ catchment areas had at least two doses of vaccine recommended by theAdvisory Committee on Immunization Practices and that the information was recorded ina registry’s database. Data from 42 grantees indicated that 46% (median=96%) of publicproviders and 13% (median=15%) of private providers had submitted records to aregistry.
Of the 43 grantees, all had implemented at least one key element on four of the12 registry functional standards (i.e., electronic data storage of core data elements,protection of confidential medical information, recovery of lost data, and consolidation of
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vaccination records from multiple providers). Three (7%) grantees reported implement-ing at least one key element in each standard. However, none had implemented all keyelements of the 12 functional standards (Table 1).
Forty-one (95%) of the 43 grantees reported immunization registry links with at leastone other health-care program; of these, 25 (61%) were linked to their state’s vital recordsdepartment. Links to birth certificates indicate that these registries are population-based(not provider-based or practice-based). The median number of weeks from birth toestablishing a registry record was 5 weeks (range: 1–12 weeks).
TABLE 1. Number and percentage of immunization jurisdictions (grantees*) withimmunization registries that have implemented key elements of the 12 essentialfunctional standards — United States, April 1999
Registries meetingRegistries meeting one or more
all key elements key elementsFunctional standard No. (%) No. (%)
Electronically store data on all NationalVaccine Advisory Committee-approvedcore data elements 30 (70) 43 (100)
Establish a registry record within 2 monthsof birth for each newborn child residingin the catchment area 31 (72) 31 ( 72)
Enable providers to retrieve informationfrom the registry on all vaccinationrecords at the time of encounter 38 (88) 38 ( 88)
Ensure that providers submit informationon all vaccination encounters within1 month of vaccine administration 41 (95) 41 ( 95)
Protect confidential medical information(confidentiality and security measures) 3 ( 7 ) 43 (100)
Recover lost data (disaster recovery) 21 (49) 43 (100)Exchange vaccination records using
Health Level 7 standards 3 ( 7 ) 4 ( 9 )Automatically determine the vaccination(s)
needed when a person seeksvaccination based on AdvisoryCommittee on Immunization Practices’recommendations 35 (81) 35 ( 81)
Identify persons late for vaccinationto provide recall notifications 25 (58) 37 ( 86)
Automatically produce vaccinationcoverage reports by providers,age groups, and geographic areas 33 (77) 38 ( 88)
Produce authorized vaccination records 37 (86) 37 ( 86)Consolidate vaccination records from
multiple providers, using duplication andedit checking procedures to optimizeaccuracy and completeness 7 (16) 43 (100)
*Of the 64 grantees, 43 have implemented immunization registries.
276 MMWR April 7, 2000
Immunization Registries — Continued
Reported by: Systems Development Br, Data Management Div, National Immunization Pro-gram, CDC.
Editorial Note: The 1999 IRAR represents the first attempt to quantify and evaluatestate-based and community-based immunization registry development in the UnitedStates. These data suggest that substantial progress has been made in U.S. communitiesand states in enrolling children, recruiting providers, and implementing registry functionalstandards.
Substantial challenges remain in developing registries. One of the greatest chal-lenges is balancing the need to protect the privacy of patients, providers, and other usersof these systems with the need to gather and share information to protect the publichealth and provide clinical benefit to persons. In response to recommendations of theNational Vaccine Advisory Committee (NVAC) 1999 report, Development of Community-and State-Based Immunization Registries (1 ), CDC developed specifications for privacyprotection of registry participants and for the confidentiality of information contained ina registry. These specifications were approved by NVAC in February 2000. They areconsistent with privacy regulations required by the Health Insurance Portability andAccountability Act of 1996 (3 ).
Houston
New York City
Philadelphia
District of Columbia
San Antonio
Commonwealths/Territories
Cities*
U.S. Virgin Islands
American Samoa
Palau
†
Registries Providing Insufficient Data(n= 6)
1%–49%(n=17)
>80%(n=14)
-Testing RegistriesPlanning or Pilot
(n= 6) 50%–79%
Percentage of Children with Immunization Histories in a Registry
(n=16)
FIGURE 1. States, cities, and commonwealths/territories with children aged 0–5 yearswith at least two vaccine doses recorded in an immunization registry — United States,April 1999
* No report received from Chicago.† The Marshall Islands, Micronesia, and the Northern Mariana Islands reported no registry
activity. No report received from Guam.
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Ensuring high levels of public and private provider participation in registries is acritical prerequisite to complete and accurate electronic vaccination records. In an in-creasingly mobile environment, where approximately 20% of children move by age2 years (4 ), appropriate vaccination decision-making often depends on aggregatingvaccination histories from multiple providers. Solving technical and operational chal-lenges of sharing vaccination information between registries that may use differentcomputer hardware and software is critical.
The findings in this report are subject to at least two limitations. First, because theIRAR relies on self-reported data, some bias is expected. On-site verification of thesedata is planned to ensure a more accurate assessment of registry development. Second,because only immunization program grantees were surveyed, these data underesti-mate the degree of registry activity occurring in the United States. Survey respondentsreported 84 additional immunization registries implemented at the local level. However,data collected on these systems suggest that many are not population-based.
Since 1994, more than $178 million in federal funds have been awarded to state andlocal health departments to support the development and implementation of immuniza-tion registries (5 ). Fiscal savings associated with registries include avoiding duplicativevaccinations, assuring maximal returns for appointments through the use of reminder/recall notices, reducing vaccine wastage, avoiding manual generation of vaccinationcertificates, and avoiding manual review of multiple records to establish the Health PlanEmployer Data and Information Set (HEDIS) indices. Immunization registries also canplay an important role in increasing vaccine safety and monitoring adverse eventsbecause core registry data elements include vaccine date and type, manufacturer, andlot number. Registry data in Arkansas and California have been used to identify andrevaccinate children who received vaccinations from sub-potent vaccine lots or an inad-equate dosage of vaccine (6,7 ), and Oklahoma’s registry data have been used to monitorthe implementation of new vaccine recommendations (8 ). In addition, immunizationregistry links to broader child health information systems may help coordinate preven-tive care by enabling provider assessments of other health needs. Funding sources needto be identified to ensure reaching the Healthy People 2010 immunization registry objec-tive (2 ). Additional information on immunization registries is available from CDC’s immu-nization registry World-Wide Web site, http://www.cdc.gov/nip/registry; telephone (800)799-7062; or e-mail, [email protected]. The National Vaccine Advisory Committee. Development of community- and state-based
immunization registries, January 12, 1999. Available at: http://www.cdc.gov/nip/registry.Accessed January 1999.
2. US Department of Health and Human Services. Healthy people 2010 (Conference ed., vol 1).Washington, DC: US Department of Health and Human Services, January 2000. Available athttp://www.health.gov/healthypeople. Accessed January 24, 2000.
3. US Department of Health and Human Services. Standards for privacy of individually iden-tifiable health information: proposed rule, November 3, 1999 (45 CFR parts 160 through164). Federal Register 1999;64:59917–66.
4. Fowler MG, Simpson GA, Schoendorf KC. Families on the move and children’s healthcare,May 1993. Pediatrics 1993;91:934–40.
5. All Kids Count. Sustaining financial support for immunization registries. Decatur, Georgia:All Kids Count, September 1999.
6. Fowler K. An immunization registry provider feedback module—the missing link inregistries: an Arkansas case example. Presented at the 2000 Immunization RegistryConference, Newport, Rhode Island, March 27–29, 2000.
7. Fontanesi J. Registry cost/benefit issues. Proceedings of the 33rd National ImmunizationConference, Dallas, Texas, June 22–25, 1999.
8. Blose D. Using registries to monitor the implementation of new vaccine recommendations.Presented at the 2000 Immunization Registry Conference, Newport, Rhode Island, March27–29, 2000.
Palmar Pallor as an Indicator for Anthelminthic TreatmentAmong Ill Children Aged 2–4 Years — Western Kenya, 1998
Infections with the soil-transmitted intestinal helminths (i.e., Ascaris lumbricoides,Trichuris trichiura, and hookworm), estimated to affect approximately 1 billion persons,are among the most common and widespread human infections (1 ). Among childrenaged <5 years, intestinal helminth infections cause malnutrition and anemia, two impor-tant causes of mortality. Anthelminthic treatment (deworming) improves nutritional sta-tus of school-aged children (1 ). The World Health Organization and the United NationsChildren’s Fund (UNICEF) have developed guidelines that include interventions for ane-mia and malnutrition (2 ) in the integrated management of childhood illness (IMCI) forchildren aged <5 years seen at first-level health-care facilities in developing countries.Under the IMCI guidelines, in geographic areas where hookworm or Trichuris infectionsare endemic, children aged 2–4 years with palmar pallor are treated with an anthelmin-thic drug. This report summarizes an investigation of the use of palmar pallor as anindication for anthelminthic treatment among ill children aged 2–4 years seen at first-level health-care facilities in rural western Kenya; the investigation found that palmarpallor was associated with anemia but not with intestinal helminth infection.
Children eligible for enrollment in the investigation were aged 2–4 years and seen forthe first consultation for an illness during July 13–August 12, 1998, in three rural govern-ment health-care facilities in Bungoma District, Kenya. Enrollment criteria included care-taker consent, absence of a severe illness requiring referral, and no reported anthelmin-thic treatment during the 6 months preceding the investigation based on an interviewwith the caretaker. Each child was examined using IMCI guidelines, and a standard ques-tionnaire was used to collect demographic, socioeconomic, and clinical information.Hemoglobin (Hb) levels were measured from a capillary finger-stick blood specimenusing a hemoglobin photometer. Blood smears were examined for malaria parasites.Stool samples were processed using a formal-ethyl-acetate concentration technique(3 ). The intensity of helminth infection was measured by eggs per gram of stool andcategorized as light, moderate, or heavy (3 ).
Of the 633 eligible children, 574 (91%) were enrolled; 34 (5%) children were excludedfor receiving anthelminthic treatment during the 6 months before the investigation,13 (2%) for the presence of a severe illness requiring referral, and 12 (2%) because thecaretaker refused to participate. Excluded and enrolled children had similar demographicand socioeconomic characteristics. The participants’ median age was 37 months (range:24–59 months); 319 (56%) were boys. A total of 191 (33%) children had palmar pallor,351 (61%) children had anemia (Hb: <11 gm/dL; normal: 11–16 gm/dL),329 (57%) had malaria parasitemia, 32 (6%) were infected with Ascaris, 34 (6%) wereinfected with hookworm, and five (1%) were infected with Trichuris; 66 (12%) childrenhad one or more intestinal helminths.
Immunization Registries — Continued
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The prevalence of helminth infection was 10% among children aged 2 years, 11%among children aged 3 years, and 16% among children aged 4 years. All Trichurisinfections, 97% of hookworm infections, and 78% of Ascaris infections were of lightintensity. The sensitivity, specificity, and positive predictive value (PPV) of palmar palloras an indicator for anemia were 50%, 93%, and 92%, respectively. Palmar pallor wasassociated with anemia (prevalence ratio [PR]=2.0; 95% confidence interval [CI]=1.8–2.3); however, no association was found between palmar pallor and helminth infection(Table 1). The sensitivity, specificity, and PPV of palmar pallor for identifying children withhelminth infections were 27%, 66%, and 9%, respectively. Although malaria parasitemiamodified the association between palmar pallor and helminth infection, the sensitivityand PPV of palmar pallor as an indicator for helminth infections in this geographic arearemained low in children with or without malaria parasitemia. In the IMCI guidelines, theanthelminthic treatment is specifically for anemia; however, no association was foundbetween palmar pallor and hookworm or Trichuris infection (PR=0.9; 95% CI=0.5–1.8).The sensitivity, specificity, and PPV of palmar pallor for identifying children with hook-worm or Trichuris infection were 32%, 67%, and 6%, respectively.Reported by: CN Wamae, Kenya Medical Research Institute; J Mwanza, S Makama, Ministry ofHealth, Nairobi, Kenya. International Child Survival and Emerging Infections Program SupportActivity and Epidemiology Br, Div of Parasitic Diseases, National Center for InfectiousDiseases; and an EIS Officer, CDC.
Editorial Note: The prevalence of intestinal helminth infection among a population of illchildren aged 2–4 years who resided in Bungoma District, Kenya, was low and theinfections identified were of low intensity. Findings of the few prevalence studies ofintestinal helminth infection among healthy preschool-aged children in tropical areasare higher, ranging from 25% to 90% (4–7 ). The prevalence of intestinal helminthinfections among healthy children aged 4–5 years in Kisumu District, western Kenya,was 60% (7 ) compared with 16% among children aged 4 years seen for outpatient carein Bungoma District; therefore, wide variation may exist in the prevalence of helminth
TABLE 1. Association between palmar pallor and intestinal helminth infectionamong ill children aged 2–4 years — Bungoma District, Kenya, 1998
Helminth Positiveinfection Prevalence predictive
Characteristic Yes N o ratio (95% CI*) Sensitivity Specificity value
infections within proximate geographic areas. These differences may be environmental(e.g., Kisumu and Bungoma districts are only 62 miles [100 km] apart; however, KisumuDistrict is warmer and more humid than Bungoma District) or socioeconomic (e.g., theprevalence of Ascaris and Trichuris infections among school children living inovercrowded conditions in Colombo, Sri Lanka, was seven to 10 times higher than thatamong children attending rural schools approximately 20 miles [30 km] away) (8 ).
The findings in this report indicate that palmar pallor was predictive of anemia butwas not associated with helminth infections. Heavy hookworm infections consistentlyhave been reported to be associated with anemia (9,10 ). The lack of associationbetween palmar pallor and helminth infection in Bungoma District may be the result ofthe light intensity of hookworm infections; all but one hookworm infection was consid-ered light.
The findings in this report are subject to at least two limitations. First, children whoparticipated in the study may not be representative of all ill children in Bungoma District.Second, the findings may not be generalizable beyond areas with low prevalence andintensity of helminth infections.
Most children in Bungoma District with a helminth infection would not have receivedanthelminthic treatment, and few receiving anthelminthic treatment would have beeninfected with an intestinal helminth if palmar pallor were used to indicate anthelminthictreatment, as recommended in the IMCI guidelines. These guidelines have been intro-duced into approximately 60 developing countries; although implementing the guide-lines provides a means for delivering the nutritional benefits of anthelminthic therapy topreschool-aged children, additional studies may help to determine under what condi-tions palmar pallor indicates the need for anthelminthic treatment. These studies shouldbe conducted in areas with varying prevalences of intestinal helminth and malariainfections.References
1. Stephenson LS. Impact of helminth infections on human nutrition. New York: Taylor andFrancis, 1987.
2. Gove S. Integrated management of childhood illness by outpatient health workers: tech-nical basis and overview. Bull World Health Organ 1997;75(Suppl 1):7–24.
3. Beach MJ, Streit TG, Addiss DG, Prospere R, Roberts JM, Lammie PJ. Assessment ofcombined ivermectin and albendazole for treatment of intestinal helminth and Wuchere-ria bancrofti infections in Haitian schoolchildren. Am J Trop Med Hyg 1999;60:479–86.
4. de Silva NR, de Silva HJ, Jaypani VP. Intestinal parasites in the Kandy area, Sri Lanka.Southeast Asian J Trop Med Public Health 1994;25:469–73.
5. Martin J, Keymer A, Isherwood RJ, Wainwright SM. The prevalence and intensity ofAscaris lumbricoides infections in Moslem children from northern Bangladesh. Trans RSoc Trop Med Hyg 1983;77:702–6.
6. Gupta MC, Urrutia JJ. Effect of periodic anti-ascariasis and anti-Giardia treatment onnutritional status of pre-school children. Am J Clin Nutr 1982;36:79–86.
7. Olsen A. The proportion of helminth infections in a community in western Kenya whichwould be treated by mass chemotherapy of school children. Trans R Soc Trop Med Hyg1998;92:144–8.
8. Atukorala TMS, Laneroole P. Soil-transmitted helminthic infection and its effect on nutri-tional status of adolescent schoolgirls of low socioeconomic status in Sri Lanka. J TropPed 1999;45:18–22.
9. Stoltzfus RJ, Albinoco M, Chwaya HM, Tielsch JM, Schulze KJ, Savioli L. Effects of Zanzi-bar school-based deworming program on iron status of children. Am J Clin Nutr 1998;68:179–86.
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10. Brooker S, Peshu N, Warn PA, et al. The epidemiology of hookworm infection and itscontribution to anemia among pre-school children on the Kenya coast. Trans R Soc TropMed Hyg 1999;93:240–6.
Community Indicators of Health-Related Quality of Life —United States, 1993–1997
It is known that persons’ longevity is affected by the environmental and populationcharacteristics of their community (1–3 ). Studies that identify community-level charac-teristics associated with the health-related quality of life (HRQOL) of residents could helpguide local health planning. Data from the Behavioral Risk Factor Surveillance System(BRFSS) for 1993–1997 indicate that HRQOL differs among U.S. counties according tocounty population size. In addition, socioeconomic and health status indicators, such aspoverty, noncompletion of high school, unemployment, number of persons with severework disabilities, mortality, and births to adolescents, also might affect county-levelHRQOL differences. This report examines initial findings on the relation between se-lected community health status indicators (CHSIs) and the mean number of days thatpersons aged �18 years reported ill health (i.e., unhealthy days), a surveillance measureof population HRQOL (4–6 ). The findings suggest that CHSIs may be useful in the publichealth planning process.
Since 1993, CDC and participating state health departments have tracked the num-ber of days persons aged �18 years have reported feeling unhealthy through BRFSS, anongoing, state-based, random-digit–dialed telephone survey of the civilian,noninstitutionalized U.S. population aged �18 years. Unhealthy days were measuredusing the sum of the responses to two questions about the estimated number of daysduring the 30 days preceding the survey when the respondent’s physical health (i.e.,“physical illness and injury”) or mental health (i.e., “stress, depression, and problemswith emotions”) was not good, with the restriction that unhealthy days for an individualcould not exceed 30 days (6 ). The mean number of unhealthy days was estimated foreach U.S. county after each response was weighted to the age, race, and sex distributionof the state in which the county was located. Data from 1993 through 1997 were com-bined to increase the precision of the estimates of the mean number of unhealthy daysper county. Data from 2450 (80%) of 3081 U.S. counties were analyzed; Alaska and631 counties with <20 BRFSS respondents were excluded from the analysis.
Potential county indicators of HRQOL were selected from preliminary CHSI data pro-vided for this analysis by the Public Health Foundation (PHF)* based on recognized
*County data for age distribution, population size and density, poverty, high school graduation,unemployment, severe work disabilities, all-cause mortality, and births to adolescents wereobtained from the Health Resources and Services Administration-funded Community HealthStatus Indicator Project Health Status Reports, which were created by the CHSI Projectpartners (Association of State and Territorial Health Officials, National Association of Countyand City Health Officials, and PHF). The CHSI Project is described by PHF at http://www.phf.org. References to sites of non-CDC organizations on the Internet are provided asa service to MMWR readers and do not constitute or imply endorsement of theseorganizations or their programs by CDC or the U.S. Department of Health and Human Services.CDC is not responsible for the content of pages found at these sites.
associations with HRQOL (6 ) or on their possible relation to population HRQOL (i.e.,mortality rate and births to adolescents). Socioeconomic and health status indicators(specifically, rates of poverty, high school education, unemployment, severe workdisability, mortality, and proportion of births to adolescents) were analyzed for meanpopulation HRQOL differences among counties categorized by population size and theprevalence level of each indicator. Multiple linear regression was used to estimate thepercentage of variability in the mean number of unhealthy days per county explained bythese indicators after weighting county records by the square root of the BRFSS samplesize to allow use of county data with smaller BRFSS sample sizes and to reflect theincreased precision of HRQOL estimates in counties with larger sample sizes. A maxi-mum relative weight of 6.32 (i.e., the square root of 800 divided by the square root of 20)was assigned to counties with �800 respondents.
Overall, persons aged �18 years reported an average of 5.3 unhealthy days (range:0.7–12.7 days) during the 30 days preceding the survey (Table 1). The most unhealthydays were reported by persons in the most populous counties (i.e., 5.6 unhealthy days forcounties of �1,000,000); the least unhealthy days were reported by persons in countieswith populations of 500,000–999,999 (5.1 days). Compared with the latter group, personsin smaller and larger counties were estimated to have 1.3 million excess unhealthy yearsof life. For each CHSI indicator, counties in the lowest third (i.e., the one third that had thelowest rates for poverty, noncompletion of high school education, unemployment,severe work disability, mortality, and proportion of births to adolescents) had the lowestmean number of unhealthy days overall and for almost all county sizes. Taking all testedindicators together, the variability in county unhealthy days predicted was approximately11%. Socioeconomic and health-related factors accounted for almost all of the predictedvariability; age and population size and density accounted for only 0.4%.Reported by: N Kanarek, PhD, D Sockwell, MSPH, Public Health Foundation, Washington, DC.H Jia, PhD, Univ of Tennessee, Knoxville. The following BRFSS coordinators: S Reese, MPH,Alabama; P Owen, Alaska; B Bender, MBA, Arizona; G Potts, MBA, Arkansas; B Davis, PhD,California; M Leff, MSPH, Colorado; M Adams, MPH, Connecticut; F Breukelman, Delaware;I Bullo, District of Columbia; S Hoecherl, Florida; L Martin, MS, Georgia; F Reyes-Salvail, MS,Hawaii; J Aydelotte, MA, Idaho; B Steiner, MS, Illinois; L Stemnock, Indiana; J Igbokwe, PhD,Iowa; C Hunt, MPH, Kansas; T Sparks, Kentucky; B Bates, MSPH, Louisiana; D Maines, Maine;A Weinstein, MA, Maryland; D Brooks, MPH, Massachusetts; H McGee, MPH, Michigan;N Salem, PhD, Minnesota; D Johnson, MS, Mississippi; T Murayi, PhD, Missouri; P Feigley,PhD, Montana; L Andelt, PhD, Nebraska; E DeJan, MPH, Nevada; Larry Powers, MA, NewHampshire; G Boeselager, MS, New Jersey; W Honey, MPH, New Mexico; C Baker, New York;Z Gizlice, PhD, North Carolina; L Shireley, MPH, North Dakota; P Pullen, Ohio; K Baker, MPH,Oklahoma; K Pickle, MS, Oregon; L Mann, Pennsylvania; Y Cintron, MPH, Puerto Rico; J Hesser,PhD, Rhode Island; M Wu, MD, South Carolina; M Gildemaster, South Dakota; D Ridings,Tennessee; K Condon, Texas; K Marti, Utah; C Roe, MS, Vermont; K Carswell, MPH, Virginia;K Wynkoop-Simmons, PhD, Washington; F King, West Virginia; K Pearson, Wisconsin; M Futa,MA, Wyoming. Health Care and Aging Studies Br, Div of Adult and Community Health, NationalCenter for Chronic Disease Prevention and Health Promotion, CDC.
Editorial Note: Local health agencies play a major role in promoting health and quality oflife, and community indicators of HRQOL can help to guide planning programs to improvecommunity health. This initial study of community indicators of HRQOL predictedapproximately 11% of the variability in unhealthy days among counties. Although nosimilar county-based HRQOL studies are known, the amount of variability explainedwas similar to that found in efforts to predict health-care costs of various populationsusing socioeconomic and health-related indicators (7 ). Although counties with
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TABLE 1. Number* of counties† and mean number of unhealthy days§ in persons aged �18 years, by county population¶
and prevalence of socioeconomic and health characteristics — United States, Behavioral Risk Factor SurveillanceSystem, 1993–1997
Population<25,000 25,000–49,999 50,000–99,999 100,000–499,999 500,000–999,999 �1,000,000 All counties
No. No. No. No. No. No. No.Characteristics/Level counties Mean counties Mean counties Mean counties Mean counties Mean counties Mean counties Mean
Upper (�6.6%) 359 5.6 215 5.5 108 5.3 88 5.6 10 5.1 2 5.9 782 5.5Middle (4.2%–6.5%) 283 5.4 201 5.2 150 5.3 168 5.1 28 5.2 18 5.7 848 5.4Lower (�4.1%) 355 5.0 151 4.9 117 4.9 151 5.0 31 4.9 14 5.4 819 5.1* n=2450.† Counties with �20 Behavioral Risk Factor Surveillance System (BRFSS) respondents to questions about unhealthy days for 1993–1997.§ Mean number of unhealthy days for all adult respondents in each county when standard BRFSS weights are used.¶ Bureau of the Census estimates for mid-1997.
** 1995 Bureau of the Census Small Area Income Poverty estimates.†† Calculated using 1990 Census of Population and Housing, STF3A, Bureau of the Census area resource file data.§§ Persons with no employment, were available for work, and had made efforts to find employment. Current Population Survey, Local Area Unemployment Statistics,
Bureau of Labor Statistics, U.S. Department of Labor.¶¶ Borawski EA, Jia H, Wu GW, Case Western Reserve University. The use of the Behavioral Risk Factor Surveillance System (BRFSS) to estimate the prevalence
of state and substate disability. Atlanta, Georgia: U.S. Department of Health and Human Services, Public Health Service, CDC, 1999.*** Per 100,000 population. Average annual rate for all causes of death, age adjusted to 2000. Data from CDC’s National Center for Health Statistics (5-year average,
1993–1997).††† Data from CDC’s National Center for Health Statistics, Vital Statistics Reporting System (5-year average for 1993–1997). One county with a population of <25,000
has a missing value for this percentage.
Health
-Related
Qu
ality of Life —
Co
ntin
ued
Vol. 49 / No. 13 MMWR 285
Health-Related Quality of Life — Continued
populations of 500,000–999,999 residents reported better HRQOL than the other counties,this study indicates that counties of all sizes might be able to address factors to reduceadult unhealthy days.
The findings in this report are subject to at least five limitations. First, BRFSS reachesonly persons who have a telephone and are able and willing to participate in the survey;therefore, results may underestimate the number of unhealthy days experienced bypersons living at home and do not reflect persons living in long-term–care facilities orother institutions. Second, unhealthy days may be overestimated for some persons whoreport both physical and mental unhealthy days. Third, the county indicators explored inthis study were few, cross-sectional, and not necessarily the most valid and sensitiveindicators of population HRQOL. Fourth, the analysis was limited by the small BRFSSsample size available at the county level, and BRFSS data are weighted to reflect theirstate’s population characteristics, which may differ from population characteristics of thecounty. Finally, although one scheme for weighting counties in the regression analysiswas used, others should be explored.
Using a validated HRQOL measure, this study represents an initial effort to quantifycertain factors that contribute to the well-being of populations in U.S. counties (8 ). How-ever, to improve county health planning, additional factors that contribute directly toHRQOL, such as access to health care and preventive services, environmental factors,workplace safety, public safety, and health behaviors, should be assessed. Also, countyhealth departments should use local HRQOL data and associated community indicatorsto identify health issues and guide their community health improvement process (9,10 ).References
1. Dever GEA. Community health analysis: global awareness at the local level. Gaithersburg,Maryland: Aspen Publishers, 1991.
2. Murray CJ, Michaud CM, McKenna MT, Marks JS. U.S. patterns of mortality by county andrace: 1965–1994. Cambridge, Massachusetts: Harvard Center for Population and Develop-ment Studies; Atlanta, Georgia: US Department of Health and Human Services, CDC,1998.
3. Yen IH, Syme SL. The social environment and health: a discussion of the epidemiologicalliterature. Annu Rev Public Health 1999;20:287–308.
4. US Department of Health and Human Services. Healthy people 2010 (Conference ed., vol1 and 2). Washington, DC: US Department of Health and Human Services, January 2000.Available at http://www.health.gov/healthypeople. Accessed March 20, 2000.
5. Hennessy CH, Moriarty DG, Zack MM, Scherr PA, Brackbill R. Measuring health-relatedquality of life for public health surveillance. Public Health Rep 1994;109:665–72.
6. CDC. State differences in reported healthy days among adults—United States, 1993–1996. MMWR 1998;47:239–44.
7. Ettner SL, Frank RG, McGuire TG, Newhouse JP, Notman EH. Risk adjustment of mentalhealth and substance abuse payments. Inquiry 1998;35:223–39.
8. Moriarty D, Zack M. Validation of the Centers for Disease Control and Prevention’s healthydays measures [Abstract]. In: Quality of Life Research, Abstracts Issue, Sixth AnnualConference of the International Society for Quality of Life Research, Barcelona, Spain,1999.
9. Durch JS, Bailey LA, Stoto MA. Improving health in the community: a role for performancemonitoring. Washington, DC: National Academy of Sciences Press, 1997. Available athttp://www.nap.edu. Accessed March 20, 2000.
10. Last J. Public health and human ecology. Stamford, Connecticut: Appleton and Lange,1998.
In the article “Public Opinion About Public Health—United States, 1999,” there wereerrors in the percentages given in both tables. On page 259 in Table 1, in the “Sinusproblems/allergies” category, the percentages for “Not too important,” “Not at all,” and“Don’t know” should have been 4%, 3%, and 4%, respectively. On page 260 in Table 2, inthe “Air pollution” category, the percentages for “Not much,” “Not at all,” and “Don’tknow” should have been 5%, 2%, and 5%, respectively.
In the Notice to Readers “National Vaccine Program Office Workshop on Aluminum inVaccines” on page 262, the web address was incorrect. It should have been http://www.cdc.gov/od/nvpo/calendar.htm.
Erratum: Vol. 49, No. 10
In the article “Preliminary FoodNet Data on the Incidence of Foodborne Illnesses—Selected Sites, United States, 1999,” in Table 1 on page 203, the total rate for 1998 isincorrect. The total should read “46.9.”
FIGURE I. Selected notifiable disease reports, comparison of provisional 4-week totalsending April 1, 2000, with historical data — United States
*No measles cases were reported for the current 4-week period, yielding a ratio for week13 of zero (0).
† Ratio of current 4-week total to mean of 15 4-week totals (from previous, comparable, andsubsequent 4-week periods for the past 5 years). The point where the hatched area beginsis based on the mean and two standard deviations of these 4-week totals.
TABLE I. Summary — provisional cases of selected notifiable diseases,United States, cumulative, week ending April 1, 2000 (13th Week)
Cum. 2000 Cum. 2000
Anthrax - HIV infection, pediatric*§ 32Brucellosis* 6 Plague 2Cholera - Poliomyelitis, paralytic -Congenital rubella syndrome 1 Psittacosis* 4Cyclosporiasis* 3 Rabies, human -Diphtheria - Rocky Mountain spotted fever (RMSF) 30Encephalitis: California* serogroup viral 2 Streptococcal disease, invasive Group A 767
-:no reported cases *Not notifiable in all states. † Updated weekly from reports to the Division of Viral and Rickettsial Diseases, National Center for Infectious Diseases (NCID). § Updated monthly from reports to the Division of HIV/AIDS Prevention–Surveillance and Epidemiology, National Center for HIV,
STD, and TB Prevention (NCHSTP), last update March 26, 2000. ¶ Updated from reports to the Division of STD Prevention, NCHSTP.
DISEASE DECREASE INCREASECASES CURRENT
4 WEEKS
Ratio (Log Scale)†
Beyond Historical Limits
4210.250.1250.0625
613
301
103
42
0
151
26
180
6
Hepatitis A
Hepatitis B
Hepatitis, C/Non-A, Non-B
Legionellosis
Measles, Total*
Mumps
Pertussis
Rubella
Meningococcal Infections
0.50.03125
288 MMWR April 7, 2000
TABLE II. Provisional cases of selected notifiable diseases, United States,weeks ending April 1, 2000, and April 3, 1999 (13th Week)
Guam 13 1 - 120 - - N N U UP.R. 187 413 142 U - - - 2 U UV.I. 16 10 - U - U - U U UAmer. Samoa - - - U - U - U U UC.N.M.I. - - - U - U - U U U
N: Not notifiable U: Unavailable -: no reported cases C.N.M.I.: Commonwealth of Northern Mariana Islands* Individual cases may be reported through both the National Electronic Telecommunications System for Surveillance (NETSS) and the Public
Health Laboratory Information System (PHLIS).† Updated monthly from reports to the Division of HIV/AIDS Prevention–Surveillance and Epidemiology, National Center for HIV, STD, and
TB Prevention, last update March 26, 2000.§ Chlamydia refers to genital infections caused by C. trachomatis. Totals reported to the Division of STD Prevention, NCHSTP.
Guam - - - - - 16 U UP.R. - - 6 21 10 93 U UV.I. - U - U - U U UAmer. Samoa - U - U - U U UC.N.M.I. - U - U - U U U
N: Not notifiable U: Unavailable -: no reported cases*Individual cases may be reported through both the National Electronic Telecommunications System for Surveillance (NETSS) and the Public Health Laboratory Information System (PHLIS).
TABLE II. (Cont’d) Provisional cases of selected notifiable diseases, United States,weeks ending April 1, 2000, and April 3, 1999 (13th Week)
Vol. 49 / No. 13 MMWR 291
TABLE II. (Cont’d) Provisional cases of selected notifiable diseases, United States,weeks ending April 1, 2000, and April 3, 1999 (13th Week)
Guam - 3 U U - - - -P.R. 1 18 U U 20 59 - 41V.I. - U U U - U - UAmer. Samoa - U U U - U - UC.N.M.I. - U U U - U - UN: Not notifiable U: Unavailable -: no reported cases*Individual cases may be reported through both the National Electronic Telecommunications System for Surveillance (NETSS) and the Public Health Laboratory Information System (PHLIS).
†Cumulative reports of provisional tuberculosis cases for 1999 are unavailable (“U”) for some areas using the Tuberculosis Information System(TIMS).
292 MMWR April 7, 2000
TABLE III. Provisional cases of selected notifiable diseases preventableby vaccination, United States, weeks ending April 1, 2000,
Guam - - - 2 - 2 - - - - - -P.R. - - 15 38 8 54 - - - - - -V.I. - U - U - U U - U - - UAmer. Samoa - U - U - U U - U - - UC.N.M.I. - U - U - U U - U - - UN: Not notifiable U: Unavailable - : no reported cases*For imported measles, cases include only those resulting from importation from other countries.†Of 63 cases among children aged <5 years, serotype was reported for 26 and of those, 5 were type b.
Guam - - - - 1 - - 1 - - -P.R. - 7 - - - - - - - - -V.I. - U U - U U - U U - UAmer. Samoa - U U - U U - U U - UC.N.M.I. - U U - U U - U U - UN: Not notifiable U: Unavailable - : no reported cases
294 MMWR April 7, 2000
TABLE IV. Deaths in 122 U.S. cities,* week endingApril 1, 2000 (13th Week)
�65 45-64 25-44 1-24 <1Reporting Area
All Causes, By Age (Years)
AllAges
P&I†
Total� � � � ��65 45-64 25-44 1-24 <1
Reporting Area
All Causes, By Age (Years)
AllAges
P&I†
Total
NEW ENGLAND 450 327 80 31 8 4 42Boston, Mass. 143 95 28 15 3 2 17Bridgeport, Conn. 31 23 6 2 - - 1Cambridge, Mass. 6 5 - 1 - - 2Fall River, Mass. 24 22 1 1 - - 1Hartford, Conn. U U U U U U ULowell, Mass. 22 16 5 1 - - 1Lynn, Mass. 11 10 1 - - - 2New Bedford, Mass. 29 24 5 - - - -New Haven, Conn. 31 20 7 2 2 - 2Providence, R.I. U U U U U U USomerville, Mass. 3 2 1 - - - -Springfield, Mass. 48 32 11 2 1 2 7Waterbury, Conn. 41 33 4 3 1 - 3Worcester, Mass. 61 45 11 4 1 - 6
U: Unavailable -:no reported cases*Mortality data in this table are voluntarily reported from 122 cities in the United States, most of which have populations of 100,000 or more.A death is reported by the place of its occurrence and by the week that the death certificate was filed. Fetal deaths are not included.
†Pneumonia and influenza.§Because of changes in reporting methods in this Pennsylvania city, these numbers are partial counts for the current week. Complete countswill be available in 4 to 6 weeks.
¶Total includes unknown ages.
Vol. 49 / No. 13 MMWR 295
Contributors to the Production of the MMWR (Weekly)Weekly Notifiable Disease Morbidity Data and 122 Cities Mortality Data
Samuel L. Groseclose, D.V.M., M.P.H.
State Support Team CDC Operations TeamRobert Fagan Carol M. KnowlesJose Aponte Deborah A. AdamsPaul Gangarosa, M.P.H. Willie J. AndersonGerald Jones Patsy A. HallDavid Nitschke Pearl SharpCarol A. Worsham Kathryn Snavely
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IU.S. Government Printing Office: 2000-533-206/28002 Region IV
Director, Centers for DiseaseControl and Prevention
Jeffrey P. Koplan, M.D., M.P.H.
Acting Deputy Director for Scienceand Public Health, Centers forDisease Control and Prevention
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